class_docstr: Large-scale distributed sampling face recognition training Module, of course sampling is an option,
  There will be no loss of accuracy in sampling in large-scale identities training tasks, uses only
  8 NVIDIA RTX2080Ti to complete classification tasks with 10 millions of identities, 64 NVIDIA
  RTX2080Ti can complete classification tasks with 100 million of identities.
  
  See the original paper:
  https://arxiv.org/abs/2203.15565
  
  Parameters
  ----------
  symbol: Symbol
      Backbone symbol.
  fc7_model: Object
      Object of margin loss.
  memory_bank: Memory bank Object.
      Object of memory bank, which maintain local class centers and their momentum.
  memory_optimizer: Optimizer object.
      The updater of memory bank, default is sgd optimizer.
  logger:
code: def forward(self, data_batch, is_train=None):
      self.backbone_module.forward(data_batch, is_train=is_train)
      if is_train:
          self.num_update += 1
          fc1 = self.backbone_module.get_outputs()[0]
          label = data_batch.label[0]
  
          total_features = self.allgather(
              tensor=fc1,
              name="total_feature",
              shape=(self.batch_size * self.size, self.embedding_size),
              dtype="float32",
              context=self.gpu,
          )
          total_labels = self.allgather(
              tensor=label,
              name="total_label",
              shape=(self.batch_size * self.size,),
              dtype="int32",
              context=self.cpu,
          )
          return total_features, total_labels
      else:
          return None
  
imports: import logging
  import warnings
  from collections import namedtuple
  import horovod.mxnet
  import mxnet
  import mxnet.ndarray
  import numpy
  from default import config
  from optimizer import DistributedOptimizer
label_desc: Use image node_id 11 for calling the SampleDistributeModule obj's underlying member method code with example usage: obj.forward(data_batch, is_train) and returns: total_features,  total_labels, None